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Lfm2Moe

docs/source/en/model_doc/lfm2_moe.md

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This model was released on {release_date} and added to Hugging Face Transformers on 2025-10-07.

Lfm2Moe

Overview

LFM2-MoE is a Mixture-of-Experts (MoE) variant of LFM2. The LFM2 family is optimized for on-device inference by combining short‑range, input‑aware gated convolutions with grouped‑query attention (GQA) in a layout tuned to maximize quality under strict speed and memory constraints.

LFM2‑MoE keeps this fast backbone and introduces sparse MoE feed‑forward networks to add representational capacity without significantly increasing the active compute path. The first LFM2-MoE release is LFM2-8B-A1B, with 8.3B total parameters and 1.5B active parameters. The model excels in quality (comparable to 3-4B dense models) and speed (faster than other 1.5B class models).

Example

The following example shows how to generate an answer using the AutoModelForCausalLM class.

python
from transformers import AutoModelForCausalLM, AutoTokenizer


# Load model and tokenizer
model_id = "LiquidAI/LFM2-8B-A1B"
model = AutoModelForCausalLM.from_pretrained(
    model_id,
    device_map="auto",
    dtype="bfloat16",
#    attn_implementation="flash_attention_2" <- uncomment on compatible GPU
)
tokenizer = AutoTokenizer.from_pretrained(model_id)

# Generate answer
prompt = "What is C. elegans?"
input_ids = tokenizer.apply_chat_template(
    [{"role": "user", "content": prompt}],
    add_generation_prompt=True,
    return_tensors="pt",
    tokenize=True,
).to(model.device)

output = model.generate(
    input_ids,
    do_sample=True,
    temperature=0.3,
    min_p=0.15,
    repetition_penalty=1.05,
    max_new_tokens=512,
)

print(tokenizer.decode(output[0], skip_special_tokens=False))

Lfm2MoeConfig

[[autodoc]] Lfm2MoeConfig

Lfm2MoeForCausalLM

[[autodoc]] Lfm2MoeForCausalLM

Lfm2MoeModel

[[autodoc]] Lfm2MoeModel - forward

Lfm2MoePreTrainedModel

[[autodoc]] Lfm2MoePreTrainedModel - forward